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. 2019 Nov 20;19(23):5077.
doi: 10.3390/s19235077.

A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios

Siji Chen  1 , Bin Shen  1 , Xin Wang  1 , Sang-Jo Yoo  2
Affiliations

A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios

Siji Chen et al. Sensors (Basel). .

Abstract

Machine learning (ML) based classification methods have been viewed as one kind of alternative solution for cooperative spectrum sensing (CSS) in recent years. In this paper, ML techniques based CSS algorithms are investigated for cognitive radio networks (CRN). Specifically, a strong machine learning classifier (MLC) and decision stumps (DS) based adaptive boosting (AdaBoost) classification mechanism is proposed for pattern classification of the primary user's behavior in the network. The conventional AdaBoost algorithm only combines multiple sub-classifiers and produces a strong weight based on their weights in classification. Taking into account the fact that the strong MLC and the weak DS serve as different sub-classifiers in classification, we propose employing a strong MLC as the first-stage classifier and DS as the second-stage classifiers, to eventually determine the class that the spectrum energy vector belongs to. We verify in simulations that the proposed hybrid AdaBoost algorithms are capable of achieving a higher detection probability than the conventional ML based spectrum sensing algorithms and the conventional hard fusion based CSS schemes.

Keywords: AdaBoost; classifier; cognitive radio network (CRN); cooperative spectrum sensing; decision stump; energy vector; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Scatter plot of energy vectors classified by K-means.
Figure 2
Figure 2
Support vector machine (SVM) model.
Figure 3
Figure 3
Scatter plot of energy vectors classified by SVM.
Figure 4
Figure 4
Flowchart of the adaptive boosting (AdaBoost) classification algorithm.
Figure 5
Figure 5
Layout of the cognitive radio networks (CRN).
Figure 6
Figure 6
Prediction error when two secondary users (SUs) participate in cooperative spectrum sensing (CSS). K-nearest neighbors (KNN), decision stumps (DS).
Figure 7
Figure 7
Prediction error when nine SUs participate in CSS.
Figure 8
Figure 8
Detection probability with desired false alarm probability 10% and multiple SUs in CSS.
See this image and copyright information in PMC

References

    1. Marzetta T.L. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wireless Commun. 2010;9:3590–3600. doi: 10.1109/TWC.2010.092810.091092. - DOI
    1. Axell E., Leus G., Larsson E.G. Overview of spectrum sensing for cognitive radio; Proceedings of the IEEE International Workshop on Cognitive Information Processing; Elba, Italy. 14–16 June 2010; pp. 322–327.
    1. Axell E., Leus G., Larsson E.G., Poor V.H. Spectrum Sensing for Cognitive Radio: State-of-the-Art and Recent Advances. IEEE Signal Process Mag. 2012;29:101–116. doi: 10.1109/MSP.2012.2183771. - DOI
    1. Awin F., Esam A.-R., Kemal T. Blind spectrum sensing approaches for interweaved cognitive radio system: A tutorial and short course. IEEE Commun. Surv. Tutorials. 2019;21:238–259. doi: 10.1109/COMST.2018.2863681. - DOI
    1. Mosleh. S., Tadaion A.A., Derakhtian M. Performance analysis of the Neyman-Pearson fusion center for spectrum sensing in a Cognitive Radio network; Proceedings of the IEEE EUROCON; St.-Petersburg, Russia. 18–23 May 2009; pp. 1420–1425.